The present invention relates generally to computing technology, and more specifically, to resource over-subscription.
Data centers may be configured to process large amounts or volumes of data. In the context of processing large amounts of volumes of data, a map-reduce algorithm may be used. The map-reduce algorithm may entail a mapping of a large data set into smaller data sets or workloads. The workloads may be processed by a plurality of machines, virtual machines, or threads, potentially in parallel, to obtain sub-processed results. The sub-processed results may ultimately be merged or combined to obtain overall results.
In the context of network computing, a resource, such as a switch, may enter a so-called “over-subscribed” state. Succinctly stated, the switch may be over-subscribed if the input data or load required to be processed or handled by the switch exceeds the output capacity of the switch. An over-subscribed resource may represent a bottleneck in a network.
To address over-subscription, additional resources (e.g., additional switches) may be allocated. However, allocating additional resources represents additional cost in terms of, e.g., money, complexity, management, etc. Moreover, over-subscription may represent a dynamic or transient condition. Thus, the additional resources may be idle a majority of the time, resulting in an underutilization of the resources. As such, a network provider or operator may elect to forego allocating the additional resources. However, if not addressed, over-subscription may result in a loss of data (e.g., data packets). A loss of data may be reflected in terms of degraded network quality or reliability.